Land use classification of high resolution remote sensing images using an encoder based modified GAN architecture?

The development of new deep learning algorithms has brought in a significant change in land use classification. Earlier models in remote sensing image classification were mainly based on machine learning architectures. Many recent models combined deep neural network structures for feature extraction and machine learning algorithms for classification. There are also some end to end deep learning architectures used for feature extraction and classification in the area of remote sensing image classification. Anyway such models are known to take more training data to yield good classification performance and they also necessitate more extensive technical support. The proposed model uses an encoder based modified Generative Adversarial Network (GAN) architecture for land use classification which can yield better results with less amount of training samples. GAN is shown to be a superior synthesiser and classifier than other deep learning models because it can work with a limited number of dataset samples. The validation of the proposed model is carried out on UC Merced and AID land use datasets. The classification results support the hypothesis that the proposed model can outperform conventional land use classification models.

Automated summary

The development of new deep learning algorithms has significantly changed land use classification. Recent models combine deep neural network structures with machine learning algorithms for feature extraction and classification. The proposed model based on the modified GAN architecture can achieve better results with fewer training samples, making it superior to other deep learning models.